This invention relates to computer/machine vision. More specifically, a system of machine vision is disclosed for the real time measurement of lint and trash being processed in a cotton gin. The system provides the ability to quantify the amount of trash and seed cotton/lint without detaining or impeding the flow through the cotton gin at any time.
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Modem cotton gins have the purpose of extracting lint (the cotton) from trash and seeds- usually the sticks, leaves and burrs that are entrained with the cotton. These modem gins include many individual machine components that are operated sequentially to form the gin processing line. The components are often specific in the types of trash that they remove. Stick machines, inclined cleaners, and especially lint cleaners process the lint to a purity where it can be baled and delivered to spinning mills.
Unfortunately, the cotton processed by such machines varies widely in trash content. For example, stripper harvested cotton has trash content in the range of 30% by total weight of the seed cotton processed, where if the same cotton is stripper harvested with the addition of a field cleaner on the harvester, it may come in with only a 15% trash weight. Even larger trash fluctuations can be observed in regions that are running both stripper and picker harvesters, as the picker harvesters will only have a trash contents in the range of 5%. Due to these different harvesting techniques, the same gin can xe2x80x9cseexe2x80x9d and process both types of cotton. As a consequence, and depending upon the trash content of the cotton processed, various components of a cotton gin are either left in the serial process combination, or are taken out of the serial process combination. When most cotton gins no longer require their individual components to be configured in series to process cotton for the optimum removal of trash and seed from the lint, the same cotton gin components can also be configured to operate parallel processing lines in order to increase throughput. It is therefore highly desirable to have the cotton gin immediately responsive in its configuration to the trash level of the lint being processed.
It is to be understood that it is customary to over clean the cotton resulting in an economic loss of the valuable line that gets removed at each cleaning apparatus.
Moreover, new growing techniques are also having an impact. For example, new planting techniques utilize ultra narrow rows that can only be harvested with xe2x80x9cstripperxe2x80x9d harvesters.
It is known that running unnecessarily certain components of a cotton gin can be inefficient to the total economic efficiency of the gin. Trash removed from cotton inevitably extracts lint. And as a general rule, the later in the process the particular piece of cleaning machinery is located, the greater the loss of lint with extracted trash. By way of example, an inclined stick machine placed before ginning of the seed from seed cotton cause lint loss in the range of 0.5%. At the same time, the running of a lint cleaner can cause losses in the range of 20% of the lint. It therefore becomes extremely important to know and understand when a particular component within a cotton gin can be idled while having the output of the gin meet the required quality standard for the ultimately produced lint cotton.
This need to produce a better quality product for sale to the cotton textile mills and to reduce labor costs during processing has led to considerable interest in process control for cotton gins. Anthony and Byler (1994) indicate that process control can range from $15,000 to $100,000. Most of the work to date has involved the online measurement of moisture and trash. Anthony (1990) reported on a system, which used a dynamic programming model along with black and white video trash sensors to determine the optimum number of lint cleaners needed to optimize the returns to the grower.
It is inevitable that the cotton gins in the near future will become fully computerized and automated (Byler and Anthony, 1997). This is due to the fact that optimal control of the gin will produce optimal economic returns for a given ginned bale of cotton (Bennett et al, 1997). This will be advantageous to the growers, the ginners, and the processing mills as they will receive a consistent product that can be tailored to their desired specifications. In this regard, expect the gins to become fully automated in the near future as this technology becomes available. It has already been shown that this automation will utilize some form of trash measurement system at several key locations scattered throughout the ginning process.
Improved machine vision is required. Further, such machine vision will encounter widely varying conditions. For example, the majority of cotton produced in Texas is stripper-harvested. This inexpensive harvesting technique results in large amounts of trash contamination of the seed cotton. The current cleaning techniques present a tradeoff between trash removal and loss of the valuable lint. It has been recognized that adjusting the number of lint cleanings can maximize the profit. The optimum number of lint cleanings can be determined if the trash content and lint turnout is known (Baker, 1994).
One of the major problems facing producers and ginners in the stripper-harvested areas is the presence of large variations in the trash content levels. Additionally the recent innovation of the field cleaner for stripper harvesters has intensified this variation. I feel that this variation leads to a wide range of optimal gin machinery settings for stripper harvested seed cotton cleaning.
Byler and Anthony (1997) reported on a computer-based cotton color and trash measurement system that was used to control the drying and cleaning machinery selection. This system utilizes a global calorimetric color sensor that measures the average color of the imaging area. In addition to the color sensor is a black and white video camera for measurement of the trash particles. A sampling system that presents a solid piece of lint (no voids or holes) and at a uniform packing density to remove the lint shadows is requisite for proper system function. At the time that this system was installed at a gin in Cortland, Ala., it was reported to be the most complete computerized gin process control system in the world. This process control system utilized two trash level sensors. The cotton color/trash sensors were based upon the High-Volume-Instruments (HVI) that are used in the classing office. The first sensor was located opposite of a ram located in the back of the feed control. The ram was periodically extended to press cotton against a glass sample imaging plate. The second color/trash/moisture measurement station was located behind the gin stand and before the lint cleaners. A paddle sampler was used to obtain a sample from the duct and press the sample against a viewing window.
Anthony (1989) reported that sample compression against an imaging window was used to increase the sample density in order to produce a more repeatable image by minimizing the shadows. The coefficient of determination was reported to be r2=0.62 and r2=0.72 for the two trash measurement stations located at the feed control. The sample compression was felt to be important enough that several devices were developed to accomplish this and U.S. Pat. No. 5,125,279 Jun. 30, 1992 entitled System for Analyzing Cotton was obtained for a paddle sampler to accomplish the sample compression for the trash, moisture and color measurement. It is still in use to date in the Zellweger Uster Intelligin and was reported to be fully functional in two commercial gin""s as conducted in a USDA study (Anthony et al, 1995).
The modem classing methods use High-volume-Instruments (HVI) systems to measure trash content and lint color. A composite instrument measures the trash content and the lint color. The composite instrument is composed of a black and white video camera for the trash content determination and a two color-filtered silicon based optical sensor to measure the two components used in the classing system: brightness (Rd) and yellowness (+b). Analysis of a two-dimensional black and white image is used to express the percent of the surface area covered by non-lint particles. The algorithm is based upon applying a reflectance threshold to the image. This turns the image into a binary image composed of only two classes, the first class composed of the lint and the second class composed of everything else: trash, holes etc (Thomasson, et al. 1997).
By carefully placing a sample on an HVI instrument with care taken to avoid voids in the samples (that will be miss-classified as trash) the system works reasonably well. However, for an automated on-line system this may not always be the case. As such this technique has the disadvantage in its inability to separate the trash from any holes that may appear in the sample when pressed up against the glass imaging plate. This results in an increased error in the measurement.
Another disadvantage to this technique is the need for pressing the cotton against a glass plate, as this restricts the possible locations where this technique can be applied in a cotton gin in addition to the very likely possibility of stoppage/blockage of the cotton flow due to system malfunctions.
In the following prior art, pressing of lint and/or seed cotton, and trash to avoid the presence of voids has been practiced.
Xu, for pressed lint cotton (note no seed cotton here) was able to show, using multi-spectral values how to partition or label pixels into the following categories or classes: spots, trash, and shadow. Xu, recognized that by the transformation to the CIE L*C*h* color space, he could use the L* value to separate the lint pixels from the trash pixels. Furthermore by using the C* value lint could be separated from spot and trash pixels. Xu realized that a simple discriminant function could be built utilizing a threshold of both L* and C* to partition the space into four regions. He thereby combined the two separations to provide the ability to distinguish and uniquely identify lint pixels, spot pixels, trash pixels, and shadow pixels.
Further analysis of the Xu discrimination technique reveals that although this technique is suitable for its intended purpose, it can not be used in the case where the background shows through the lint. Using simple threshold discrimination of two variables yields at most four possible states. All four of the possible states given the Xu discriminant function have been used by in the identification. The four possible states that can and are used in the Xu discriminate function are lint pixels, spot pixels, trash pixels, and shadow pixels. All of the possible states are accounted for, which leaves no available partitioning in which to assign a new variable to account for background pixels. See Chromatic Image Analysis for Cotton Trash and Color Measurements, Xu et al., Textile Res. J, 67(12), 881-890 (1997).
Leiberman reported the ability for shape discrimination for trash into the groups: bark, sticks, or leaves/pepper trash after partitioning the pixels into trash or lint pixels in black and white image via a threshold technique.
Occlusion of multiple light beams has also been used to measure mass flow in both 2 and 3 dimensions. The disadvantage to this technique is the inability to differentiate trash from the lint particles and the associated mass versus volume differences, which are on the order of 100% or more difference by weight. This can be readily shown through the simple observation that dried leaf particles and cotton lint cover a large area with a minimal amount of weight, yet sticks and burrs cover much smaller areas yet weigh much more. See, Evaluation of Learning Vector Quantization to Classify Cotton Trash, Lieberman et al., Opt. Engr. 36(3), 914-921 (March 1997) and Predicting Gravimetric Both in Cotton from Video Indigo, Lieberman et al., U.S.D.A. Paper No. 926052 of Jun. 21-24, 1992.
A process of utilizing machine vision for processing in a cotton gin monitors a flow of lint/or seed cotton and trash anywhere throughout the cotton gin without impeding or detaining product flow, the measurement being made in real time. A video camera or other electronic digital imaging device takes a digital multi-spectral image of the trash and lint/or seed cotton passing through the cotton gin. The multi-spectral image of the trash and lint/or seed cotton is partitioned using the spectral values into a trash portion, a lint portion, and at least a third image. In turn, at least the image of trash and lint are themselves formed into a binary image from each of the partitioned images. Thereafter, the binary images of the trash portion and the lint/or seed cotton portion are used to determine the ratio of trash to total lint/or seed cotton in the flow of the lint/or seed cotton and trash. The binary image of the trash can be separately processed to determine the relative amounts of sticks, leaves and burrs present in the trash. This enables individual component control in the serial flow of the cotton through gins and multiple similar components such as inclined cleaners, stick machines and lint cleaners. Further, the binary image of the non-shadowed lint can be utilized as an index into the original multi-spectral image so as to derive a multi-spectral image composed solely of non-shadowed lint. This multi-spectral non-shadowed lint image can be used to ascertain the true average color of the cotton lint. The average non-shadowed lint color can then be used to measure the official USDA-AMS cotton color and tinge. Further, the multi-spectral non-shadowed lint image can be spectrally analyzed to determine color magnitude and spatial frequency fluctuations of the lint. This spatial frequency fluctuation of the multi-spectral data, corresponding only to the non-shadowed lint, can be utilized to identify spots of the cotton. By coupling the trash measurement, the color/tinge measurement, with the spot measurements, these techniques provide the necessary technology thereby enabling a processing gin to value probable product output in real time. The machine measurement system can be used virtually anywhere lint and trash flows within a cotton gin including air entraining ducts, dryers, feeding belts, condensers, bat accumulations, the battery condenser, and the final bale.